MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer

HQ Liao, C Barra, ZC Zhou, X Peng, I Woodhouse, A Tailor, R Parker, A Carre, P Borrow, MJ Hogan, W Paes, LC Eisenlohr, R Mallone, M Nielsen, N Ternette*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.
Original languageEnglish
Article number661
Number of pages16
JournalNature Communications
Volume15
Issue number1
DOIs
Publication statusPublished - 22 Jan 2024

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).

Funding

We thank Prof. Masafumi Takiguchi, Kumamoto University, Japan for provision of CD4-expressing cell lines transfected with single HLA-I alleles. This work was funded by the NIH project #1R21AI153978-01 (LCE), The Leona M. and Harry B. Helmsley Charitable Trust project #1901-03689 and Agence Nationale de la Recherche project ANR-19-CE15-0014-01 (RM), the European Association for the Study of Diabetes FSD/JDRF/Lilly Programme on Type 1 Diabetes Research 2019 (NT, RM), Cancer Research UK Cancer Immunology project award C55884/A21045 (NT), and Cancer Research UK RadNet Centre Award C6078/A28736 (NT). ZZ was supported by a JDRF Postdoctoral Fellowship 3-PDF-2020-942-A-N. Some immunopeptidomic datasets employed for this study were generated as part of studies supported by Medical Research Council (MRC) programme grant MR/K012037, grants from the National Institutes of Health (UM1 AI 100645 and R01 AI 118549) and by AMED grant 17FK0410302H0003 for AIDS research (PB). The computational aspects of this research were supported by the Wellcome Trust Core Award Grant Number 203141/Z/16/Z and the NIHR Oxford BRC. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. PB is a Jenner Institute Investigator.

FundersFunder number
Cancer Research UK Cancer ImmunologyC55884/A21045
Cancer Research UK RadNet CentreC6078/A28736
European Association
National Institutes of Health1R21AI153978-01, UM1 AI 100645, R01 AI 118549
National Institutes of Health
Leona M. and Harry B. Helmsley Charitable Trust1901-03689
Juvenile Diabetes Research Foundation United Kingdom
Japan Agency for Medical Research and Development17FK0410302H0003
Wellcome Trust203141/Z/16/Z
Medical Research CouncilMR/K012037
National Institute for Health and Care Research
Agence Nationale de la RechercheANR-19-CE15-0014-01

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